Scoring function (SF),
which measures the plausibility of triplets
in knowledge graphs (KGs),
plays an important role to learn from KGs [1]}, [2]}.
To fairly evaluate different SFs,
open graph benchmark (OGB) [3]} sets up leaderboards
to evaluate the different models on large-scale graphs.
Among the two leaderboards for link prediction task in KG,
we observe that the leading models on ogbl-wikikg2
have similar forms by modeling embeddings as translations in vector spaces.
To analyze the success of these SFs
and try to design a better SF,
we are motivated to use automated machine learning (AutoML) techniques [4]}, [5]}, [6]}
to search SFs in this type.
